# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import copy
import inspect
import json
import os
import random
import tempfile
import unittest
from importlib import import_module
from typing import List, Tuple

from huggingface_hub import delete_repo, login
from requests.exceptions import HTTPError
from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import tooslow  # noqa: F401
from transformers.testing_utils import (
    PASS,
    USER,
    CaptureLogger,
    _tf_gpu_memory_limit,
    is_pt_tf_cross_test,
    is_staging_test,
    require_tf,
    require_tf2onnx,
    slow,
    torch_device,
)
from transformers.utils import logging


logger = logging.get_logger(__name__)


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import (
        TF_MODEL_FOR_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_MASKED_LM_MAPPING,
        TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
        TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
        TF_MODEL_FOR_PRETRAINING_MAPPING,
        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
        TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
        BertConfig,
        TFAutoModel,
        TFAutoModelForSequenceClassification,
        TFBertModel,
        TFSharedEmbeddings,
        tf_top_k_top_p_filtering,
    )
    from transformers.generation_tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
    )

    if _tf_gpu_memory_limit is not None:
        gpus = tf.config.list_physical_devices("GPU")
        for gpu in gpus:
            # Restrict TensorFlow to only allocate x GB of memory on the GPUs
            try:
                tf.config.set_logical_device_configuration(
                    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                )
                logical_gpus = tf.config.list_logical_devices("GPU")
                print("Logical GPUs", logical_gpus)
            except RuntimeError as e:
                # Virtual devices must be set before GPUs have been initialized
                print(e)


def _config_zero_init(config):
    configs_no_init = copy.deepcopy(config)
    for key in configs_no_init.__dict__.keys():
        if "_range" in key or "_std" in key:
            setattr(configs_no_init, key, 0.0)
    return configs_no_init


@require_tf
class TFModelTesterMixin:

    model_tester = None
    all_model_classes = ()
    all_generative_model_classes = ()
    test_mismatched_shapes = True
    test_resize_embeddings = True
    test_head_masking = True
    is_encoder_decoder = False
    has_attentions = True

    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict:
        inputs_dict = copy.deepcopy(inputs_dict)

        if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
            inputs_dict = {
                k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1))
                if isinstance(v, tf.Tensor) and v.ndim > 0
                else v
                for k, v in inputs_dict.items()
            }

        if return_labels:
            if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
                inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
                inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
                inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in [
                *get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
                *get_values(TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING),
            ]:
                inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in get_values(TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING):
                inputs_dict["next_sentence_label"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32)
            elif model_class in [
                *get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
                *get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING),
                *get_values(TF_MODEL_FOR_MASKED_LM_MAPPING),
                *get_values(TF_MODEL_FOR_PRETRAINING_MAPPING),
                *get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING),
                *get_values(TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING),
            ]:
                inputs_dict["labels"] = tf.zeros(
                    (self.model_tester.batch_size, self.model_tester.seq_length), dtype=tf.int32
                )
        return inputs_dict

    def test_initialization(self):
        pass

    def test_save_load(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname, saved_model=False)
                model = model_class.from_pretrained(tmpdirname)
                after_outputs = model(self._prepare_for_class(inputs_dict, model_class))

                self.assert_outputs_same(after_outputs, outputs)

    def test_save_load_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))
            model_config = model.get_config()
            # make sure that returned config is jsonifiable, which is required by keras
            json.dumps(model_config)
            new_model = model_class.from_config(model.get_config())
            # make sure it also accepts a normal config
            _ = model_class.from_config(model.config)
            _ = new_model(self._prepare_for_class(inputs_dict, model_class))  # Build model
            new_model.set_weights(model.get_weights())
            after_outputs = new_model(self._prepare_for_class(inputs_dict, model_class))

            self.assert_outputs_same(after_outputs, outputs)

    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.call)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]

            if model.config.is_encoder_decoder:
                expected_arg_names = [
                    "input_ids",
                    "attention_mask",
                    "decoder_input_ids",
                    "decoder_attention_mask",
                ]
                expected_arg_names.extend(
                    ["head_mask", "decoder_head_mask"] if "head_mask" and "decoder_head_mask" in arg_names else []
                )
                # Necessary to handle BART with newly added cross_attn_head_mask
                expected_arg_names.extend(
                    ["cross_attn_head_mask", "encoder_outputs"]
                    if "cross_attn_head_mask" in arg_names
                    else ["encoder_outputs"]
                )
                self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)

            else:
                expected_arg_names = ["input_ids"]
                self.assertListEqual(arg_names[:1], expected_arg_names)

    def test_onnx_compliancy(self):
        if not self.test_onnx:
            return

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        INTERNAL_OPS = [
            "Assert",
            "AssignVariableOp",
            "EmptyTensorList",
            "ReadVariableOp",
            "ResourceGather",
            "TruncatedNormal",
            "VarHandleOp",
            "VarIsInitializedOp",
        ]
        onnx_ops = []

        with open(os.path.join(".", "utils", "tf_ops", "onnx.json")) as f:
            onnx_opsets = json.load(f)["opsets"]

        for i in range(1, self.onnx_min_opset + 1):
            onnx_ops.extend(onnx_opsets[str(i)])

        for model_class in self.all_model_classes:
            model_op_names = set()

            with tf.Graph().as_default() as g:
                model = model_class(config)
                model(model.dummy_inputs)

                for op in g.get_operations():
                    model_op_names.add(op.node_def.op)

            model_op_names = sorted(model_op_names)
            incompatible_ops = []

            for op in model_op_names:
                if op not in onnx_ops and op not in INTERNAL_OPS:
                    incompatible_ops.append(op)

            self.assertEqual(len(incompatible_ops), 0, incompatible_ops)

    @require_tf2onnx
    @slow
    def test_onnx_runtime_optimize(self):
        if not self.test_onnx:
            return

        import onnxruntime
        import tf2onnx

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            model(model.dummy_inputs)

            onnx_model_proto, _ = tf2onnx.convert.from_keras(model, opset=self.onnx_min_opset)

            onnxruntime.InferenceSession(onnx_model_proto.SerializeToString())

    def test_keras_save_load(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        tf_main_layer_classes = set(
            module_member
            for model_class in self.all_model_classes
            for module in (import_module(model_class.__module__),)
            for module_member_name in dir(module)
            if module_member_name.endswith("MainLayer")
            # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
            and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")]
            for module_member in (getattr(module, module_member_name),)
            if isinstance(module_member, type)
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(module_member, "_keras_serializable", False)
        )
        for main_layer_class in tf_main_layer_classes:
            # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
            if "T5" in main_layer_class.__name__:
                # Take the same values than in TFT5ModelTester for this shared layer
                shared = TFSharedEmbeddings(99, 32, name="shared")
                config.use_cache = inputs_dict.pop("use_cache", None)
                main_layer = main_layer_class(config, embed_tokens=shared)
            else:
                main_layer = main_layer_class(config)

            symbolic_inputs = {
                name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items()
            }

            model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs))
            outputs = model(inputs_dict)

            with tempfile.TemporaryDirectory() as tmpdirname:
                filepath = os.path.join(tmpdirname, "keras_model.h5")
                model.save(filepath)
                if "T5" in main_layer_class.__name__:
                    model = tf.keras.models.load_model(
                        filepath,
                        custom_objects={
                            main_layer_class.__name__: main_layer_class,
                            "TFSharedEmbeddings": TFSharedEmbeddings,
                        },
                    )
                else:
                    model = tf.keras.models.load_model(
                        filepath, custom_objects={main_layer_class.__name__: main_layer_class}
                    )
                assert isinstance(model, tf.keras.Model)
                after_outputs = model(inputs_dict)
                self.assert_outputs_same(after_outputs, outputs)

    def assert_outputs_same(self, after_outputs, outputs):
        # Make sure we don't have nans
        if isinstance(after_outputs, tf.Tensor):
            out_1 = after_outputs.numpy()
        elif isinstance(after_outputs, dict):
            out_1 = after_outputs[list(after_outputs.keys())[0]].numpy()
        else:
            out_1 = after_outputs[0].numpy()
        out_2 = outputs[0].numpy()
        self.assertEqual(out_1.shape, out_2.shape)
        out_1 = out_1[~np.isnan(out_1)]
        out_2 = out_2[~np.isnan(out_2)]
        max_diff = np.amax(np.abs(out_1 - out_2))
        self.assertLessEqual(max_diff, 1e-5)

    @is_pt_tf_cross_test
    def test_pt_tf_model_equivalence(self):
        import torch

        import transformers

        def prepare_pt_inputs_from_tf_inputs(tf_inputs_dict):

            pt_inputs_dict = {}
            for name, key in tf_inputs_dict.items():
                if type(key) == bool:
                    pt_inputs_dict[name] = key
                elif name == "input_values":
                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                elif name == "pixel_values":
                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                elif name == "input_features":
                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.float32)
                else:
                    pt_inputs_dict[name] = torch.from_numpy(key.numpy()).to(torch.long)

            return pt_inputs_dict

        def check_outputs(tf_outputs, pt_outputs, model_class, names):
            """
            Args:
                model_class: The class of the model that is currently testing. For example, `TFBertModel`,
                    TFBertForMaskedLM`, `TFBertForSequenceClassification`, etc. Currently unused, but it could make
                    debugging easier and faster.

                names: A string, or a tuple of strings. These specify what tf_outputs/pt_outputs represent in the model outputs.
                    Currently unused, but in the future, we could use this information to make the error message clearer
                    by giving the name(s) of the output tensor(s) with large difference(s) between PT and TF.
            """

            # Some issue (`about past_key_values`) to solve (e.g. `TFPegasusForConditionalGeneration`) in a separate PR.
            if names == "past_key_values":
                return

            # Allow `list` because `(TF)TransfoXLModelOutput.mems` is a list of tensors.
            if type(tf_outputs) in [tuple, list]:
                self.assertEqual(type(tf_outputs), type(pt_outputs))
                self.assertEqual(len(tf_outputs), len(pt_outputs))
                if type(names) == tuple:
                    for tf_output, pt_output, name in zip(tf_outputs, pt_outputs, names):
                        check_outputs(tf_output, pt_output, model_class, names=name)
                elif type(names) == str:
                    for idx, (tf_output, pt_output) in enumerate(zip(tf_outputs, pt_outputs)):
                        check_outputs(tf_output, pt_output, model_class, names=f"{names}_{idx}")
                else:
                    raise ValueError(f"`names` should be a `tuple` or a string. Got {type(names)} instead.")
            elif isinstance(tf_outputs, tf.Tensor):
                self.assertTrue(isinstance(pt_outputs, torch.Tensor))

                tf_outputs = tf_outputs.numpy()
                pt_outputs = pt_outputs.detach().to("cpu").numpy()

                tf_nans = np.isnan(tf_outputs)
                pt_nans = np.isnan(pt_outputs)

                pt_outputs[tf_nans] = 0
                tf_outputs[tf_nans] = 0
                pt_outputs[pt_nans] = 0
                tf_outputs[pt_nans] = 0

                max_diff = np.amax(np.abs(tf_outputs - pt_outputs))
                self.assertLessEqual(max_diff, 1e-5)
            else:
                raise ValueError(
                    f"`tf_outputs` should be a `tuple` or an instance of `tf.Tensor`. Got {type(tf_outputs)} instead."
                )

        def check_pt_tf_models(tf_model, pt_model):

            # send pytorch model to the correct device
            pt_model.to(torch_device)

            # Check predictions on first output (logits/hidden-states) are close enough given low-level computational differences
            pt_model.eval()

            pt_inputs_dict = prepare_pt_inputs_from_tf_inputs(tf_inputs_dict)
            pt_inputs_dict_maybe_with_labels = prepare_pt_inputs_from_tf_inputs(tf_inputs_dict_maybe_with_labels)

            # send pytorch inputs to the correct device
            pt_inputs_dict = {
                k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v for k, v in pt_inputs_dict.items()
            }
            pt_inputs_dict_maybe_with_labels = {
                k: v.to(device=torch_device) if isinstance(v, torch.Tensor) else v
                for k, v in pt_inputs_dict_maybe_with_labels.items()
            }

            # Original test: check without `labels`
            with torch.no_grad():
                pt_outputs = pt_model(**pt_inputs_dict)
            tf_outputs = tf_model(tf_inputs_dict)

            tf_keys = tuple([k for k, v in tf_outputs.items() if v is not None])
            pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])

            self.assertEqual(tf_keys, pt_keys)
            check_outputs(tf_outputs.to_tuple(), pt_outputs.to_tuple(), model_class, names=tf_keys)

            # check the case where `labels` is passed
            has_labels = any(
                x in tf_inputs_dict_maybe_with_labels for x in ["labels", "next_sentence_label", "start_positions"]
            )
            if has_labels:

                with torch.no_grad():
                    pt_outputs = pt_model(**pt_inputs_dict_maybe_with_labels)
                tf_outputs = tf_model(tf_inputs_dict_maybe_with_labels)

                # Some models' output class don't have `loss` attribute despite `labels` is used.
                # TODO: identify which models
                tf_loss = getattr(tf_outputs, "loss", None)
                pt_loss = getattr(pt_outputs, "loss", None)

                # Some PT models return loss while the corresponding TF models don't (i.e. `None` for `loss`).
                #   - TFFlaubertWithLMHeadModel
                #   - TFFunnelForPreTraining
                #   - TFElectraForPreTraining
                #   - TFXLMWithLMHeadModel
                # TODO: Fix PT/TF diff -> remove this condition to fail the test if a diff occurs
                if not ((tf_loss is None and pt_loss is None) or (tf_loss is not None and pt_loss is not None)):
                    if model_class.__name__ not in [
                        "TFFlaubertWithLMHeadModel",
                        "TFFunnelForPreTraining",
                        "TFElectraForPreTraining",
                        "TFXLMWithLMHeadModel",
                        "TFTransfoXLLMHeadModel",
                    ]:
                        self.assertEqual(tf_loss is None, pt_loss is None)

                tf_keys = tuple([k for k, v in tf_outputs.items() if v is not None])
                pt_keys = tuple([k for k, v in pt_outputs.items() if v is not None])

                # TODO: remove these 2 conditions once the above TODOs (above loss) are implemented
                # (Also, `TFTransfoXLLMHeadModel` has no `loss` while `TransfoXLLMHeadModel` return `losses`)
                if tf_keys != pt_keys:
                    if model_class.__name__ not in [
                        "TFFlaubertWithLMHeadModel",
                        "TFFunnelForPreTraining",
                        "TFElectraForPreTraining",
                        "TFXLMWithLMHeadModel",
                        "TFTransfoXLLMHeadModel",
                    ]:
                        self.assertEqual(tf_keys, pt_keys)

                # Since we deliberately make some tests pass above (regarding the `loss`), let's still try to test
                # some remaining attributes in the outputs.
                # TODO: remove this block of `index` computing once the above TODOs (above loss) are implemented
                # compute the 1st `index` where `tf_keys` and `pt_keys` is different
                index = 0
                for _ in range(min(len(tf_keys), len(pt_keys))):
                    if tf_keys[index] == pt_keys[index]:
                        index += 1
                    else:
                        break
                if tf_keys[:index] != pt_keys[:index]:
                    self.assertEqual(tf_keys, pt_keys)

                # Some models require extra condition to return loss. For example, `(TF)BertForPreTraining` requires
                # both`labels` and `next_sentence_label`.
                if tf_loss is not None and pt_loss is not None:

                    # check anything else than `loss`
                    keys = tuple([k for k in tf_keys])
                    check_outputs(tf_outputs[1:index], pt_outputs[1:index], model_class, names=keys[1:index])

                    # check `loss`

                    # tf models returned loss is usually a tensor rather than a scalar.
                    # (see `hf_compute_loss`: it uses `tf.keras.losses.Reduction.NONE`)
                    # Change it here to a scalar to match PyTorch models' loss
                    tf_loss = tf.math.reduce_mean(tf_loss).numpy()
                    pt_loss = pt_loss.detach().to("cpu").numpy()

                    tf_nans = np.isnan(tf_loss)
                    pt_nans = np.isnan(pt_loss)
                    # the 2 losses need to be both nan or both not nan
                    self.assertEqual(tf_nans, pt_nans)

                    if not tf_nans:
                        max_diff = np.amax(np.abs(tf_loss - pt_loss))
                        self.assertLessEqual(max_diff, 1e-5)

        for model_class in self.all_model_classes:

            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

            # Output all for aggressive testing
            config.output_hidden_states = True
            if self.has_attentions:
                config.output_attentions = True

            for k in ["attention_mask", "encoder_attention_mask", "decoder_attention_mask"]:
                if k in inputs_dict:
                    attention_mask = inputs_dict[k]
                    # make sure no all 0s attention masks - to avoid failure at this moment.
                    # TODO: remove this line once the TODO below is implemented.
                    attention_mask = tf.ones_like(attention_mask, dtype=tf.int32)
                    # Here we make the first sequence with all 0s as attention mask.
                    # Currently, this will fail for `TFWav2Vec2Model`. This is caused by the different large negative
                    # values, like `1e-4`, `1e-9`, `1e-30` and `-inf` for attention mask across models/frameworks.
                    # TODO: enable this block once the large negative values thing is cleaned up.
                    # (see https://github.com/huggingface/transformers/issues/14859)
                    # attention_mask = tf.concat(
                    #     [
                    #         tf.zeros_like(attention_mask[:1], dtype=tf.int32),
                    #         tf.cast(attention_mask[1:], dtype=tf.int32)
                    #     ],
                    #     axis=0
                    # )
                    inputs_dict[k] = attention_mask

            pt_model_class_name = model_class.__name__[2:]  # Skip the "TF" at the beginning
            pt_model_class = getattr(transformers, pt_model_class_name)

            tf_model = model_class(config)
            pt_model = pt_model_class(config)

            tf_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
            tf_inputs_dict_maybe_with_labels = self._prepare_for_class(inputs_dict, model_class, return_labels=True)

            # Check we can load pt model in tf and vice-versa with model => model functions
            tf_model = transformers.load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=tf_inputs_dict)
            pt_model = transformers.load_tf2_model_in_pytorch_model(pt_model, tf_model)

            check_pt_tf_models(tf_model, pt_model)

            # Check we can load pt model in tf and vice-versa with checkpoint => model functions
            with tempfile.TemporaryDirectory() as tmpdirname:
                pt_checkpoint_path = os.path.join(tmpdirname, "pt_model.bin")
                torch.save(pt_model.state_dict(), pt_checkpoint_path)
                tf_model = transformers.load_pytorch_checkpoint_in_tf2_model(tf_model, pt_checkpoint_path)

                tf_checkpoint_path = os.path.join(tmpdirname, "tf_model.h5")
                tf_model.save_weights(tf_checkpoint_path)
                pt_model = transformers.load_tf2_checkpoint_in_pytorch_model(pt_model, tf_checkpoint_path)

            check_pt_tf_models(tf_model, pt_model)

    def test_compile_tf_model(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        max_input = getattr(self.model_tester, "max_position_embeddings", 512)
        optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0)
        loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
        metric = tf.keras.metrics.SparseCategoricalAccuracy("accuracy")

        for model_class in self.all_model_classes:
            if model_class.__name__ in ["TFSpeech2TextModel", "TFSpeech2TextForConditionalGeneration"]:
                inputs = {
                    "decoder_input_ids": tf.keras.Input(
                        batch_shape=(2, max_input),
                        name="decoder_input_ids",
                        dtype="int32",
                    ),
                    "input_features": tf.keras.Input(
                        batch_shape=(
                            2,
                            max_input,
                            self.model_tester.input_feat_per_channel * self.model_tester.input_channels,
                        ),
                        name="input_features",
                        dtype="float32",
                    ),
                }
            elif self.is_encoder_decoder:
                inputs = {
                    "decoder_input_ids": tf.keras.Input(
                        batch_shape=(2, max_input),
                        name="decoder_input_ids",
                        dtype="int32",
                    ),
                    "input_ids": tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32"),
                }
            # `pixel_values` implies that the input is an image
            elif model_class.main_input_name == "pixel_values":
                inputs = tf.keras.Input(
                    batch_shape=(
                        3,
                        self.model_tester.num_channels,
                        self.model_tester.image_size,
                        self.model_tester.image_size,
                    ),
                    name="pixel_values",
                    dtype="float32",
                )
            elif model_class.__name__ in ["TFCLIPModel"]:
                inputs = {
                    "input_ids": tf.keras.Input(batch_shape=(3, max_input), name="input_ids", dtype="int32"),
                    "pixel_values": tf.keras.Input(
                        batch_shape=(
                            3,
                            self.model_tester.vision_model_tester.num_channels,
                            self.model_tester.vision_model_tester.image_size,
                            self.model_tester.vision_model_tester.image_size,
                        ),
                        name="pixel_values",
                        dtype="float32",
                    ),
                }
            elif model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
                inputs = tf.keras.Input(batch_shape=(4, 2, max_input), name="input_ids", dtype="int32")
            else:
                inputs = tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32")

            # Prepare our model
            model = model_class(config)
            model(self._prepare_for_class(inputs_dict, model_class))  # Model must be called before saving.
            # Let's load it from the disk to be sure we can use pretrained weights
            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname, saved_model=False)
                model = model_class.from_pretrained(tmpdirname)

            outputs_dict = model(inputs)
            hidden_states = outputs_dict[0]

            # Add a dense layer on top to test integration with other keras modules
            outputs = tf.keras.layers.Dense(2, activation="softmax", name="outputs")(hidden_states)

            # Compile extended model
            extended_model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
            extended_model.compile(optimizer=optimizer, loss=loss, metrics=[metric])

    def test_keyword_and_dict_args(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            inputs = self._prepare_for_class(inputs_dict, model_class)

            outputs_dict = model(inputs)

            inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
            outputs_keywords = model(**inputs_keywords)
            output_dict = outputs_dict[0].numpy()
            output_keywords = outputs_keywords[0].numpy()

            self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6)

    def test_attention_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        config.return_dict = True
        decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", self.model_tester.seq_length)
        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", self.model_tester.seq_length)
        decoder_key_length = getattr(self.model_tester, "key_length", decoder_seq_length)
        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)

        def check_decoder_attentions_output(outputs):
            out_len = len(outputs)
            self.assertEqual(min(out_len % 2, out_len % 5), 0)  # differentiation due to newly added cross_attentions
            decoder_attentions = outputs.decoder_attentions
            self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(decoder_attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
            )

        def check_encoder_attentions_output(outputs):
            attentions = [
                t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
            ]
            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
            self.assertListEqual(
                list(attentions[0].shape[-3:]),
                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
            )

        for model_class in self.all_model_classes:
            inputs_dict["output_attentions"] = True
            inputs_dict["use_cache"] = False
            config.output_hidden_states = False
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))
            out_len = len(outputs)
            self.assertEqual(config.output_hidden_states, False)
            check_encoder_attentions_output(outputs)

            if self.is_encoder_decoder:
                model = model_class(config)
                outputs = model(self._prepare_for_class(inputs_dict, model_class))
                self.assertEqual(config.output_hidden_states, False)
                check_decoder_attentions_output(outputs)

            # Check that output attentions can also be changed via the config
            del inputs_dict["output_attentions"]
            config.output_attentions = True
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))
            self.assertEqual(config.output_hidden_states, False)
            check_encoder_attentions_output(outputs)

            # Check attention is always last and order is fine
            inputs_dict["output_attentions"] = True
            config.output_hidden_states = True
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))

            self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1), len(outputs))
            self.assertEqual(model.config.output_hidden_states, True)
            check_encoder_attentions_output(outputs)

    def test_headmasking(self):
        if not self.test_head_masking:
            return

        random.Random().seed(42)
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        random.Random().seed()

        inputs_dict["output_attentions"] = True
        config.output_hidden_states = True
        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)

            # Prepare head_mask
            def prepare_layer_head_mask(i, attention_heads, num_hidden_layers):
                if i == 0:
                    return tf.concat(
                        (tf.zeros(1, dtype=tf.float32), tf.ones(attention_heads - 1, dtype=tf.float32)), 0
                    )
                elif i == num_hidden_layers - 1:
                    return tf.concat(
                        (tf.zeros(attention_heads - 1, dtype=tf.float32), tf.ones(1, dtype=tf.float32)), 0
                    )
                else:
                    return tf.ones(attention_heads, dtype=tf.float32)

            head_mask = tf.stack(
                [
                    prepare_layer_head_mask(i, config.num_attention_heads, config.num_hidden_layers)
                    for i in range(config.num_hidden_layers)
                ],
                0,
            )

            inputs = self._prepare_for_class(inputs_dict, model_class).copy()
            inputs["head_mask"] = head_mask
            if model.config.is_encoder_decoder:
                signature = inspect.signature(model.call)
                arg_names = [*signature.parameters.keys()]
                if "decoder_head_mask" in arg_names:  # necessary diferentiation because of T5 model
                    inputs["decoder_head_mask"] = head_mask
                if "cross_attn_head_mask" in arg_names:
                    inputs["cross_attn_head_mask"] = head_mask

            outputs = model(**inputs, return_dict=True)

            def check_attentions_validity(attentions):
                # Remove Nan
                for t in attentions:
                    self.assertLess(
                        (tf.math.reduce_sum(tf.cast(tf.math.is_nan(t), tf.float32))).numpy(), (tf.size(t) / 4).numpy()
                    )  # Check we don't have more than 25% nans (arbitrary)

                attentions = [
                    tf.where(tf.math.is_nan(t), 0.0, t) for t in attentions
                ]  # remove them (the test is less complete)

                self.assertAlmostEqual(tf.math.reduce_sum(attentions[0][..., 0, :, :]).numpy(), 0.0)
                self.assertNotEqual(tf.math.reduce_sum(attentions[0][..., -1, :, :]).numpy(), 0.0)
                if len(attentions) > 2:  # encoder-decodere models have only 2 layers in each modules
                    self.assertNotEqual(tf.math.reduce_sum(attentions[1][..., 0, :, :]).numpy(), 0.0)
                self.assertAlmostEqual(tf.math.reduce_sum(attentions[-1][..., -2, :, :]).numpy(), 0.0)
                self.assertNotEqual(tf.math.reduce_sum(attentions[-1][..., -1, :, :]).numpy(), 0.0)

            if model.config.is_encoder_decoder:
                check_attentions_validity(outputs.encoder_attentions)
                check_attentions_validity(outputs.decoder_attentions)
                if "cross_attn_head_mask" in arg_names:
                    check_attentions_validity(outputs.cross_attentions)
            else:
                check_attentions_validity(outputs.attentions)

    def test_hidden_states_output(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def check_hidden_states_output(config, inputs_dict, model_class):
            model = model_class(config)
            outputs = model(self._prepare_for_class(inputs_dict, model_class))
            expected_num_layers = getattr(
                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
            )

            if model.config.is_encoder_decoder:
                encoder_hidden_states = outputs.encoder_hidden_states
                decoder_hidden_states = outputs.decoder_hidden_states

                self.assertEqual(config.output_attentions, False)
                self.assertEqual(len(encoder_hidden_states), expected_num_layers)
                self.assertListEqual(
                    list(encoder_hidden_states[0].shape[-2:]),
                    [self.model_tester.seq_length, self.model_tester.hidden_size],
                )
                self.assertEqual(len(decoder_hidden_states), expected_num_layers)
                self.assertListEqual(
                    list(decoder_hidden_states[0].shape[-2:]),
                    [self.model_tester.seq_length, self.model_tester.hidden_size],
                )
            else:
                hidden_states = outputs.hidden_states
                self.assertEqual(config.output_attentions, False)
                self.assertEqual(len(hidden_states), expected_num_layers)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]),
                    [self.model_tester.seq_length, self.model_tester.hidden_size],
                )

        for model_class in self.all_model_classes:
            inputs_dict["output_hidden_states"] = True
            check_hidden_states_output(config, inputs_dict, model_class)

            del inputs_dict["output_hidden_states"]
            config.output_hidden_states = True
            check_hidden_states_output(config, inputs_dict, model_class)

    def test_model_common_attributes(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        text_in_text_out_models = (
            get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING)
            + get_values(TF_MODEL_FOR_MASKED_LM_MAPPING)
            + get_values(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING)
        )
        speech_in_text_out_models = get_values(TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING)

        for model_class in self.all_model_classes:
            model = model_class(config)
            assert isinstance(model.get_input_embeddings(), tf.keras.layers.Layer)
            if model_class in text_in_text_out_models:
                x = model.get_output_embeddings()
                assert isinstance(x, tf.keras.layers.Layer)
                name = model.get_bias()
                assert isinstance(name, dict)
                for k, v in name.items():
                    assert isinstance(v, tf.Variable)
            elif model_class in speech_in_text_out_models:
                x = model.get_output_embeddings()
                assert isinstance(x, tf.keras.layers.Layer)
                name = model.get_bias()
                assert name is None
            else:
                x = model.get_output_embeddings()
                assert x is None
                name = model.get_bias()
                assert name is None

    def test_determinism(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            first, second = (
                model(self._prepare_for_class(inputs_dict, model_class), training=False)[0],
                model(self._prepare_for_class(inputs_dict, model_class), training=False)[0],
            )
            out_1 = first.numpy()
            out_2 = second.numpy()
            out_1 = out_1[~np.isnan(out_1)]
            out_2 = out_2[~np.isnan(out_2)]
            max_diff = np.amax(np.abs(out_1 - out_2))
            self.assertLessEqual(max_diff, 1e-5)

    def test_model_outputs_equivalence(self):

        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
            tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
            dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()

            def recursive_check(tuple_object, dict_object):
                if isinstance(tuple_object, (List, Tuple)):
                    for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                        recursive_check(tuple_iterable_value, dict_iterable_value)
                elif tuple_object is None:
                    return
                else:
                    self.assertTrue(
                        all(tf.equal(tuple_object, dict_object)),
                        msg=f"Tuple and dict output are not equal. Difference: {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}",
                    )

                recursive_check(tuple_output, dict_output)

        for model_class in self.all_model_classes:
            model = model_class(config)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs)

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(model, tuple_inputs, dict_inputs, {"output_attentions": True})

            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
            check_equivalence(
                model, tuple_inputs, dict_inputs, {"output_hidden_states": True, "output_attentions": True}
            )

    def test_inputs_embeds(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)

            inputs = copy.deepcopy(inputs_dict)

            if not self.is_encoder_decoder:
                input_ids = inputs["input_ids"]
                del inputs["input_ids"]
            else:
                encoder_input_ids = inputs["input_ids"]
                decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
                del inputs["input_ids"]
                inputs.pop("decoder_input_ids", None)

            if not self.is_encoder_decoder:
                inputs["inputs_embeds"] = model.get_input_embeddings()(input_ids)
            else:
                inputs["inputs_embeds"] = model.get_input_embeddings()(encoder_input_ids)
                inputs["decoder_inputs_embeds"] = model.get_input_embeddings()(decoder_input_ids)

            inputs = self._prepare_for_class(inputs, model_class)

            model(inputs)

    def test_numpy_arrays_inputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def prepare_numpy_arrays(inputs_dict):
            inputs_np_dict = {}
            for k, v in inputs_dict.items():
                if tf.is_tensor(v):
                    inputs_np_dict[k] = v.numpy()
                else:
                    inputs_np_dict[k] = np.array(k)

            return inputs_np_dict

        for model_class in self.all_model_classes:
            model = model_class(config)

            inputs = self._prepare_for_class(inputs_dict, model_class)
            inputs_np = prepare_numpy_arrays(inputs)

            output_for_dict_input = model(inputs_np)
            output_for_kw_input = model(**inputs_np)
            self.assert_outputs_same(output_for_dict_input, output_for_kw_input)

    def test_resize_token_embeddings(self):
        if not self.test_resize_embeddings:
            return
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        def _get_word_embedding_weight(model, embedding_layer):
            embeds = getattr(embedding_layer, "weight", None)
            if embeds is not None:
                return embeds

            embeds = getattr(embedding_layer, "decoder", None)
            if embeds is not None:
                return embeds

            model(model.dummy_inputs)

            embeds = getattr(embedding_layer, "weight", None)
            if embeds is not None:
                return embeds

            embeds = getattr(embedding_layer, "decoder", None)
            if embeds is not None:
                return embeds

            return None

        for model_class in self.all_model_classes:
            for size in [config.vocab_size - 10, config.vocab_size + 10, None]:
                # build the embeddings
                model = model_class(config=config)
                old_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
                old_bias = model.get_bias()
                old_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())
                # reshape the embeddings
                model.resize_token_embeddings(size)
                new_input_embeddings = _get_word_embedding_weight(model, model.get_input_embeddings())
                new_bias = model.get_bias()
                new_output_embeddings = _get_word_embedding_weight(model, model.get_output_embeddings())

                # check that the resized embeddings size matches the desired size.
                assert_size = size if size is not None else config.vocab_size
                self.assertEqual(new_input_embeddings.shape[0], assert_size)

                # check that weights remain the same after resizing
                models_equal = True
                for p1, p2 in zip(old_input_embeddings.value(), new_input_embeddings.value()):
                    if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                        models_equal = False
                self.assertTrue(models_equal)

                if old_bias is not None and new_bias is not None:
                    for old_weight, new_weight in zip(old_bias.values(), new_bias.values()):
                        self.assertEqual(new_weight.shape[0], assert_size)

                        models_equal = True
                        for p1, p2 in zip(old_weight.value(), new_weight.value()):
                            if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                                models_equal = False
                        self.assertTrue(models_equal)

                if old_output_embeddings is not None and new_output_embeddings is not None:
                    self.assertEqual(new_output_embeddings.shape[0], assert_size)
                    self.assertEqual(new_output_embeddings.shape[1], old_output_embeddings.shape[1])

                    models_equal = True
                    for p1, p2 in zip(old_output_embeddings.value(), new_output_embeddings.value()):
                        if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                            models_equal = False
                    self.assertTrue(models_equal)

    def test_lm_head_model_random_no_beam_search_generate(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        input_ids = inputs_dict.get("input_ids", None)

        # iterate over all generative models
        for model_class in self.all_generative_model_classes:
            model = model_class(config)

            if config.bos_token_id is None:
                # if bos token id is not defined model needs input_ids
                with self.assertRaises(ValueError):
                    model.generate(do_sample=True, max_length=5)
                # num_return_sequences = 1
                self._check_generated_ids(model.generate(input_ids, do_sample=True))
            elif model_class.__name__ not in ["TFSpeech2TextForConditionalGeneration"]:
                # Models with non-text inputs won't work here; num_return_sequences = 1
                self._check_generated_ids(model.generate(do_sample=True, max_length=5))

            with self.assertRaises(ValueError):
                # generating multiple sequences when no beam search generation
                # is not allowed as it would always generate the same sequences
                model.generate(input_ids, do_sample=False, num_return_sequences=2)

            # num_return_sequences > 1, sample
            self._check_generated_ids(model.generate(input_ids, do_sample=True, num_return_sequences=2))

            # check bad words tokens language generation
            # create list of 1-seq bad token and list of 2-seq of bad tokens
            bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)]
            output_tokens = model.generate(
                input_ids, do_sample=True, bad_words_ids=bad_words_ids, num_return_sequences=2
            )
            # only count generated tokens
            generated_ids = output_tokens[:, input_ids.shape[-1] :]
            self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))

    def test_lm_head_model_no_beam_search_generate_dict_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        input_ids = inputs_dict.get("input_ids", None)
        if input_ids is None:
            input_ids = inputs_dict.get("input_features", None)

        # iterate over all generative models
        for model_class in self.all_generative_model_classes:
            model = model_class(config)
            output_greedy = model.generate(
                input_ids,
                do_sample=False,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )
            output_sample = model.generate(
                input_ids,
                do_sample=True,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )

            if model.config.is_encoder_decoder:
                self.assertIsInstance(output_greedy, TFGreedySearchEncoderDecoderOutput)
                self.assertIsInstance(output_sample, TFSampleEncoderDecoderOutput)
            else:
                self.assertIsInstance(output_greedy, TFGreedySearchDecoderOnlyOutput)
                self.assertIsInstance(output_sample, TFSampleDecoderOnlyOutput)

    def test_lm_head_model_random_beam_search_generate(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        input_ids = inputs_dict.get("input_ids", None)

        for model_class in self.all_generative_model_classes:
            model = model_class(config)

            if config.bos_token_id is None:
                # if bos token id is not defined model needs input_ids, num_return_sequences = 1
                self._check_generated_ids(model.generate(input_ids, do_sample=True, num_beams=2))
            else:
                # num_return_sequences = 1
                self._check_generated_ids(model.generate(do_sample=True, max_length=5, num_beams=2))

            with self.assertRaises(AssertionError):
                # generating more sequences than having beams leads is not possible
                model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2)

            # num_return_sequences > 1, sample
            self._check_generated_ids(
                model.generate(
                    input_ids,
                    do_sample=True,
                    num_beams=2,
                    num_return_sequences=2,
                )
            )
            # num_return_sequences > 1, greedy
            self._check_generated_ids(model.generate(input_ids, do_sample=False, num_beams=2, num_return_sequences=2))

            # check bad words tokens language generation
            # create list of 1-seq bad token and list of 2-seq of bad tokens
            bad_words_ids = [self._generate_random_bad_tokens(1, model), self._generate_random_bad_tokens(2, model)]
            output_tokens = model.generate(
                input_ids, do_sample=False, bad_words_ids=bad_words_ids, num_beams=2, num_return_sequences=2
            )
            # only count generated tokens
            generated_ids = output_tokens[:, input_ids.shape[-1] :]
            self.assertFalse(self._check_match_tokens(generated_ids.numpy().tolist(), bad_words_ids))

    def test_lm_head_model_beam_search_generate_dict_outputs(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        input_ids = inputs_dict.get("input_ids", None)
        if input_ids is None:
            input_ids = inputs_dict.get("input_features", None)

        # iterate over all generative models
        for model_class in self.all_generative_model_classes:
            model = model_class(config)
            output_beam_search = model.generate(
                input_ids,
                num_beams=2,
                do_sample=False,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )
            output_beam_sample = model.generate(
                input_ids,
                num_beams=2,
                do_sample=True,
                output_scores=True,
                output_hidden_states=True,
                output_attentions=True,
                return_dict_in_generate=True,
            )

            if model.config.is_encoder_decoder:
                self.assertIsInstance(output_beam_search, TFBeamSearchEncoderDecoderOutput)
                self.assertIsInstance(output_beam_sample, TFBeamSampleEncoderDecoderOutput)
            else:
                self.assertIsInstance(output_beam_search, TFBeamSearchDecoderOnlyOutput)
                self.assertIsInstance(output_beam_sample, TFBeamSampleDecoderOnlyOutput)

    def test_loss_computation(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            model = model_class(config)
            if getattr(model, "hf_compute_loss", None):
                # The number of elements in the loss should be the same as the number of elements in the label
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                added_label = prepared_for_class[
                    sorted(list(prepared_for_class.keys() - inputs_dict.keys()), reverse=True)[0]
                ]
                loss_size = tf.size(added_label)

                if model.__class__ in get_values(TF_MODEL_FOR_CAUSAL_LM_MAPPING):
                    # if loss is causal lm loss, labels are shift, so that one label per batch
                    # is cut
                    loss_size = loss_size - self.model_tester.batch_size

                # Test that model correctly compute the loss with kwargs
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                possible_input_names = {"input_ids", "pixel_values", "input_features"}
                input_name = possible_input_names.intersection(set(prepared_for_class)).pop()
                model_input = prepared_for_class.pop(input_name)

                loss = model(model_input, **prepared_for_class)[0]
                self.assertEqual(loss.shape, [loss_size])

                # Test that model correctly compute the loss with a dict
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)
                loss = model(prepared_for_class)[0]
                self.assertEqual(loss.shape, [loss_size])

                # Test that model correctly compute the loss with a tuple
                prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True)

                # Get keys that were added with the _prepare_for_class function
                label_keys = prepared_for_class.keys() - inputs_dict.keys()
                signature = inspect.signature(model.call).parameters
                signature_names = list(signature.keys())

                # Create a dictionary holding the location of the tensors in the tuple
                tuple_index_mapping = {0: input_name}
                for label_key in label_keys:
                    label_key_index = signature_names.index(label_key)
                    tuple_index_mapping[label_key_index] = label_key
                sorted_tuple_index_mapping = sorted(tuple_index_mapping.items())
                # Initialize a list with their default values, update the values and convert to a tuple
                list_input = []

                for name in signature_names:
                    if name != "kwargs":
                        list_input.append(signature[name].default)

                for index, value in sorted_tuple_index_mapping:
                    list_input[index] = prepared_for_class[value]

                tuple_input = tuple(list_input)

                # Send to model
                loss = model(tuple_input[:-1])[0]

                self.assertEqual(loss.shape, [loss_size])

    def test_generate_with_headmasking(self):
        attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_generative_model_classes:
            model = model_class(config)

            # We want to test only encoder-decoder models
            if not config.is_encoder_decoder:
                continue

            head_masking = {
                "head_mask": tf.zeros((config.encoder_layers, config.encoder_attention_heads)),
                "decoder_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)),
                "cross_attn_head_mask": tf.zeros((config.decoder_layers, config.decoder_attention_heads)),
            }

            signature = inspect.signature(model.call)
            if set(head_masking.keys()) < set([*signature.parameters.keys()]):
                continue

            for attn_name, (name, mask) in zip(attention_names, head_masking.items()):
                out = model.generate(
                    inputs_dict["input_ids"],
                    num_beams=1,
                    max_length=inputs_dict["input_ids"] + 5,
                    output_attentions=True,
                    return_dict_in_generate=True,
                    **{name: mask},
                )
                # We check the state of decoder_attentions and cross_attentions just from the last step
                attn_weights = out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                self.assertEqual(sum([tf.reduce_sum(w).numpy() for w in attn_weights]), 0.0)

    def test_load_with_mismatched_shapes(self):
        if not self.test_mismatched_shapes:
            return
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            if model_class not in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
                continue

            with self.subTest(msg=f"Testing {model_class}"):
                with tempfile.TemporaryDirectory() as tmp_dir:
                    model = model_class(config)
                    inputs = self._prepare_for_class(inputs_dict, model_class)
                    _ = model(**inputs)
                    model.save_pretrained(tmp_dir)

                    # Fails when we don't set ignore_mismatched_sizes=True
                    with self.assertRaises(ValueError):
                        new_model = TFAutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
                    with self.assertRaises(ValueError):
                        new_model_without_prefix = TFAutoModel.from_pretrained(tmp_dir, vocab_size=10)

                    logger = logging.get_logger("transformers.modeling_tf_utils")
                    with CaptureLogger(logger) as cl:
                        new_model = TFAutoModelForSequenceClassification.from_pretrained(
                            tmp_dir, num_labels=42, ignore_mismatched_sizes=True
                        )
                    self.assertIn("the shapes did not match", cl.out)

                    logits = new_model(**inputs).logits
                    self.assertEqual(logits.shape[1], 42)

                    with CaptureLogger(logger) as cl:
                        new_model_without_prefix = TFAutoModel.from_pretrained(
                            tmp_dir, vocab_size=10, ignore_mismatched_sizes=True
                        )
                    self.assertIn("the shapes did not match", cl.out)

                    # Although Tf models always have a prefix pointing to `MainLayer`,
                    # we still add this "without prefix" test to keep a consistency between tf and pt tests.
                    input_ids = ids_tensor((2, 8), 10)
                    if self.is_encoder_decoder:
                        new_model_without_prefix(input_ids, decoder_input_ids=input_ids)
                    else:
                        new_model_without_prefix(input_ids)

    def test_model_main_input_name(self):
        for model_class in self.all_model_classes:
            model_signature = inspect.signature(getattr(model_class, "call"))
            # The main input is the name of the argument after `self`
            observed_main_input_name = list(model_signature.parameters.keys())[1]
            self.assertEqual(model_class.main_input_name, observed_main_input_name)

    def _generate_random_bad_tokens(self, num_bad_tokens, model):
        # special tokens cannot be bad tokens
        special_tokens = []
        if model.config.bos_token_id is not None:
            special_tokens.append(model.config.bos_token_id)
        if model.config.pad_token_id is not None:
            special_tokens.append(model.config.pad_token_id)
        if model.config.eos_token_id is not None:
            special_tokens.append(model.config.eos_token_id)

        # create random bad tokens that are not special tokens
        bad_tokens = []
        while len(bad_tokens) < num_bad_tokens:
            token = tf.squeeze(ids_tensor((1, 1), self.model_tester.vocab_size), 0).numpy()[0]
            if token not in special_tokens:
                bad_tokens.append(token)
        return bad_tokens

    def _check_generated_ids(self, output_ids):
        for token_id in output_ids[0].numpy().tolist():
            self.assertGreaterEqual(token_id, 0)
            self.assertLess(token_id, self.model_tester.vocab_size)

    def _check_match_tokens(self, generated_ids, bad_words_ids):
        # for all bad word tokens
        for bad_word_ids in bad_words_ids:
            # for all slices in batch
            for generated_ids_slice in generated_ids:
                # for all word idx
                for i in range(len(bad_word_ids), len(generated_ids_slice)):
                    # if tokens match
                    if generated_ids_slice[i - len(bad_word_ids) : i] == bad_word_ids:
                        return True
        return False


def ids_tensor(shape, vocab_size, rng=None, name=None, dtype=None):
    """Creates a random int32 tensor of the shape within the vocab size."""
    if rng is None:
        rng = random.Random()

    total_dims = 1
    for dim in shape:
        total_dims *= dim

    values = []
    for _ in range(total_dims):
        values.append(rng.randint(0, vocab_size - 1))

    output = tf.constant(values, shape=shape, dtype=dtype if dtype is not None else tf.int32)

    return output


def random_attention_mask(shape, rng=None, name=None, dtype=None):
    attn_mask = ids_tensor(shape, vocab_size=2, rng=None, name=None, dtype=dtype)
    # make sure that at least one token is attended to for each batch
    attn_mask = tf.concat([tf.constant(value=1, shape=(shape[0], 1), dtype=dtype), attn_mask[:, 1:]], axis=1)
    return attn_mask


def floats_tensor(shape, scale=1.0, rng=None, name=None, dtype=None):
    """Creates a random float32 tensor"""
    if rng is None:
        rng = random.Random()

    total_dims = 1
    for dim in shape:
        total_dims *= dim

    values = []
    for _ in range(total_dims):
        values.append(rng.random() * scale)

    return tf.reshape(tf.constant(values, dtype=dtype if dtype is not None else tf.float32), shape=shape)


@require_tf
class UtilsFunctionsTest(unittest.TestCase):

    # tests whether the top_k_top_p_filtering function behaves as expected
    def test_top_k_top_p_filtering(self):
        logits = tf.convert_to_tensor(
            [
                [
                    8.2220991,  # 3rd highest value; idx. 0
                    -0.5620044,
                    5.23229752,
                    4.0386393,
                    -6.8798378,
                    -0.54785802,
                    -3.2012153,
                    2.92777176,
                    1.88171953,
                    7.35341276,  # 5th highest value; idx. 9
                    8.43207833,  # 2nd highest value; idx. 10
                    -9.85711836,
                    -5.96209236,
                    -1.13039161,
                    -7.1115294,
                    -0.8369633,
                    -5.3186408,
                    7.06427407,
                    0.81369344,
                    -0.82023817,
                    -5.9179796,
                    0.58813443,
                    -6.99778438,
                    4.71551189,
                    -0.18771637,
                    7.44020759,  # 4th highest value; idx. 25
                    9.38450987,  # 1st highest value; idx. 26
                    2.12662941,
                    -9.32562038,
                    2.35652522,
                ],  # cummulative prob of 5 highest values <= 0.6
                [
                    0.58425518,
                    4.53139238,
                    -5.57510464,
                    -6.28030699,
                    -7.19529503,
                    -4.02122551,
                    1.39337037,
                    -6.06707057,
                    1.59480517,
                    -9.643119,
                    0.03907799,
                    0.67231762,
                    -8.88206726,
                    6.27115922,  # 4th highest value; idx. 13
                    2.28520723,
                    4.82767506,
                    4.30421368,
                    8.8275313,  # 2nd highest value; idx. 17
                    5.44029958,  # 5th highest value; idx. 18
                    -4.4735794,
                    7.38579536,  # 3rd highest value; idx. 20
                    -2.91051663,
                    2.61946077,
                    -2.5674762,
                    -9.48959302,
                    -4.02922645,
                    -1.35416918,
                    9.67702323,  # 1st highest value; idx. 27
                    -5.89478553,
                    1.85370467,
                ],  # cummulative prob of 5 highest values <= 0.6
            ],
            dtype=tf.float32,
        )

        non_inf_expected_idx = tf.convert_to_tensor(
            [[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]],
            dtype=tf.int32,
        )  # expected non filtered idx as noted above

        non_inf_expected_output = tf.convert_to_tensor(
            [8.222099, 7.3534126, 8.432078, 7.4402075, 9.38451, 6.271159, 8.827531, 5.4402995, 7.3857956, 9.677023],
            dtype=tf.float32,
        )  # expected non filtered values as noted above

        output = tf_top_k_top_p_filtering(logits, top_k=10, top_p=0.6, min_tokens_to_keep=4)

        non_inf_output = output[output != -float("inf")]
        non_inf_idx = tf.cast(
            tf.where(tf.not_equal(output, tf.constant(-float("inf"), dtype=tf.float32))),
            dtype=tf.int32,
        )

        tf.debugging.assert_near(non_inf_output, non_inf_expected_output, rtol=1e-12)
        tf.debugging.assert_equal(non_inf_idx, non_inf_expected_idx)


@require_tf
@is_staging_test
class TFModelPushToHubTester(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls._token = login(username=USER, password=PASS)

    @classmethod
    def tearDownClass(cls):
        try:
            delete_repo(token=cls._token, name="test-model-tf")
        except HTTPError:
            pass

        try:
            delete_repo(token=cls._token, name="test-model-tf-org", organization="valid_org")
        except HTTPError:
            pass

    def test_push_to_hub(self):
        config = BertConfig(
            vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
        )
        model = TFBertModel(config)
        # Make sure model is properly initialized
        _ = model(model.dummy_inputs)
        with tempfile.TemporaryDirectory() as tmp_dir:
            model.save_pretrained(os.path.join(tmp_dir, "test-model-tf"), push_to_hub=True, use_auth_token=self._token)

            new_model = TFBertModel.from_pretrained(f"{USER}/test-model-tf")
            models_equal = True
            for p1, p2 in zip(model.weights, new_model.weights):
                if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                    models_equal = False
            self.assertTrue(models_equal)

    def test_push_to_hub_with_model_card(self):
        config = BertConfig(
            vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
        )
        model = TFBertModel(config)
        with tempfile.TemporaryDirectory() as tmp_dir:
            model.push_to_hub(os.path.join(tmp_dir, "test-model-tf"))
            self.assertTrue(os.path.isfile(os.path.join(tmp_dir, "test-model-card-tf", "README.md")))

    def test_push_to_hub_in_organization(self):
        config = BertConfig(
            vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
        )
        model = TFBertModel(config)
        with tempfile.TemporaryDirectory() as tmp_dir:
            model.save_pretrained(
                os.path.join(tmp_dir, "test-model-tf-org"),
                push_to_hub=True,
                use_auth_token=self._token,
                organization="valid_org",
            )

            new_model = TFBertModel.from_pretrained("valid_org/test-model-tf-org")
            models_equal = True
            for p1, p2 in zip(model.weights, new_model.weights):
                if tf.math.reduce_sum(tf.math.abs(p1 - p2)) > 0:
                    models_equal = False
            self.assertTrue(models_equal)
